Practical guides on LLM cost optimization, model routing, and AI infrastructure.
How routing LLM traffic across OpenAI, Anthropic, and Google simultaneously reduces costs, improves reliability, and doesn't require compromising on quality.
A data-driven breakdown of real production LLM traffic showing which tasks actually require frontier models — and which are burning money unnecessarily.
A practical benchmark guide for engineering teams: which tasks GPT-4o-mini handles as well as GPT-4o, and where the cost difference isn't worth the quality trade-off.
Beyond the API invoice: the real financial and operational cost of routing every LLM call to your most capable model — and the compounding effect over time.
Everything engineering teams need to know about LLM model routing — how it works, routing strategies, quality validation, and how to implement it without a codebase rewrite.
Current OpenAI API pricing for all major models, a practical cost calculator, and strategies to reduce your bill by 40–70% using intelligent model selection.
Most engineering teams are overpaying for LLM API calls by 50–70%. Here's exactly how to fix it — without touching your application code.
A technical explainer on AI inference proxies — what they do, how they differ from gateways and SDKs, and when they make sense for production LLM systems.